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A Latent Variable Model Approach to PMI-based Word Embeddings
Sanjeev Arora
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Yuanzhi Li
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Yingyu Liang
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Tengyu Ma
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Andrej Risteski
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Paper Details:
Year: 2016
Venue:
TACL |
Citations
URL
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
Dheeraj Mekala
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Vivek Gupta
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Bhargavi Paranjape
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Harish Karnick
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Word Mover’s Embedding: From Word2Vec to Document Embedding
Lingfei Wu
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Ian En-Hsu Yen
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Kun Xu
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Fangli Xu
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Avinash Balakrishnan
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Pin-Yu Chen
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Pradeep Ravikumar
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Michael J. Witbrock
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Uncovering Divergent Linguistic Information in Word Embeddings with Lessons for Intrinsic and Extrinsic Evaluation
Mikel Artetxe
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Gorka Labaka
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Iñigo Lopez-Gazpio
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Eneko Agirre
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Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features
Matteo Pagliardini
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Prakhar Gupta
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Martin Jaggi
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Skip-Gram − Zipf + Uniform = Vector Additivity
Alex Gittens
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Dimitris Achlioptas
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Michael W. Mahoney
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Information-Theory Interpretation of the Skip-Gram Negative-Sampling Objective Function
Oren Melamud
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Jacob Goldberger
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A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Mikhail Khodak
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Nikunj Saunshi
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Yingyu Liang
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Tengyu Ma
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Brandon Stewart
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Sanjeev Arora
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Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures
Prafulla Kumar Choubey
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Ruihong Huang
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Exploring Semantic Properties of Sentence Embeddings
Xunjie Zhu
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Tingfeng Li
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Gerard de Melo
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Linear Algebraic Structure of Word Senses, with Applications to Polysemy
Sanjeev Arora
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Yuanzhi Li
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Yingyu Liang
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Tengyu Ma
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Andrej Risteski
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Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline
Kawin Ethayarajh
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http://nlp.stanford.edu/projects/glove/
https://code.google.com/p/word2vec/
http://arxiv.org/abs/1502.03520
http://arxiv.org/abs/1502.03520
Field Of Study
Linguistic Trends
Discourse
Embeddings
Approach
Generative Model
Discriminative Model
Language
English
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